Thesis / Dissertation

Data stream clustering and anomaly detection

Milad Chenaghlou, Christopher Leckie (ed.)

Published : 2019

Abstract

Data stream clustering and anomaly detection have grown in importance with the advent of hardware and software technologies that capture and generate continuous streams of sensor data. Stream data mining problems are particularly important in application domains such as network intrusion detection, road traffic analysis, social media analysis and military surveillance systems. However, a number of open challenges need to be addressed in order for stream clustering and anomaly detection to be effectively used in those applications. One of the main challenges regarding data stream clustering and anomaly detection is computational efficiency. In non-stationary data streams in which patterns ch..

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